Identification of human errors and influencing factors: a machine learning approach

Caroline Morais, Ka Lai Yung, Karl Johnson, Raphael Moura, Michael Beer, Edoardo Patelli

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
99 Downloads (Pure)


The capability of learning from accidents from different industrial sectors could prevent similar accidents to happen. With this aim, the Multi-attribute Technological Accidents Dataset (MATA-D) has been created, using a classification focused on the relation between human errors and their influencing factors (e.g., cognitive functions, organisational and technological factors). The process of collecting new data for this dataset should be constant, not only to decrease epistemic uncertainty in human reliability data but also to reflect changes in human behaviour due to evolving technology and organisational arrangements. However, reading an accident report is a time-consuming process, which delays the learning process. For this reason, this research proposes an automated approach to train the computer on a predefined classification scheme (taxonomy), which will be called the virtual human factors classifier. The virtual classifier should support human experts to analyse accident reports for organisational, technological, and individual factors that may trigger human errors. The proposed approach is based on classifying text according to previously labelled accident reports by human experts. Two case studies are used to demonstrate how data from different sectors can be used to train the machine, providing an efficient cross-discipline knowledge transfer. The accuracy of the results is promising and comparable to the classifications provided by human experts. The proposed work demonstrated to the industry the feasibility of the use of artificial intelligence to collect data and support risk and reliability assessments, and recommendations based on the study findings are suggested for investigation agencies.
Original languageEnglish
Article number105528
Pages (from-to)105528
Number of pages25
JournalSafety Science
Early online date27 Oct 2021
Publication statusPublished - 28 Feb 2022


  • automated text classification
  • accident report data
  • human factors taxonomy
  • human reliability data


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